def from_dict(self, object_dict: dict) -> NDDS_Annotation_Object:
check_required_keys(object_dict,
required_keys=[
'class', 'instance_id', 'visibility',
'location', 'quaternion_xyzw',
'pose_transform', 'cuboid_centroid',
'projected_cuboid_centroid', 'bounding_box',
'cuboid', 'projected_cuboid'
])
check_required_keys(object_dict['bounding_box'],
required_keys=['top_left', 'bottom_right'])
return NDDS_Annotation_Object(
class_name=object_dict['class'],
instance_id=object_dict['instance_id'],
visibility=object_dict['visibility'],
location=Point3D.from_list(object_dict['location']),
quaternion_xyzw=Quaternion.from_list(
object_dict['quaternion_xyzw']),
pose_transform=np.array(object_dict['pose_transform']),
cuboid_centroid=Point3D.from_list(object_dict['cuboid_centroid']),
projected_cuboid_centroid=Point2D.from_list(
object_dict['projected_cuboid_centroid']),
bounding_box=BBox.from_list(
object_dict['bounding_box']['top_left'] +
object_dict['bounding_box']['bottom_right'],
input_format='pminpmax'),
cuboid=Cuboid3D.from_list(object_dict['cuboid'], demarcation=True),
projected_cuboid=Cuboid2D.from_list(
object_dict['projected_cuboid'], demarcation=True))
def adjust_center_to_src_img(self):
h, w = self.src_img.shape[:2]
if self.center is None:
self.center_to_cener_of(self.src_img)
cx, cy = self.center.to_list()
cx = cx if cx < w else w - 1
cy = cy if cy < h else h - 1
self.update_center(Point2D(x=cx, y=cy))
def update_src_coco(self, src_coco_img: COCO_Image,
src_coco_ann: COCO_Annotation):
self.update_src_coco_img(src_coco_img=src_coco_img)
self.update_src_img(src_img=cv2.imread(self.src_coco_img.coco_url))
self.update_src_coco_ann(src_coco_ann=src_coco_ann)
self.update_center(
center=Point2D.from_list(src_coco_ann.bbox.center()))
self.adjust_center_to_src_img()
self.check_ids_are_valid()
self.update_zoom()
def from_dict(cls, item_dict: dict) -> Linemod_Annotation:
return Linemod_Annotation(
data_root=item_dict['data_root'],
mask_path=item_dict['mask_path'],
depth_path=item_dict['depth_path']
if 'depth_path' in item_dict else None,
type=item_dict['type'],
class_name=item_dict['cls'],
corner_2d=Point2D_List.from_list(item_dict['corner_2d'],
demarcation=True),
corner_3d=Point3D_List.from_list(item_dict['corner_3d'],
demarcation=True),
center_2d=Point2D.from_list(item_dict['center_2d']),
center_3d=Point3D.from_list(item_dict['center_3d']),
fps_2d=Point2D_List.from_list(item_dict['fps_2d'],
demarcation=True),
fps_3d=Point3D_List.from_list(item_dict['fps_3d'],
demarcation=True),
K=LinemodCamera.from_matrix(np.array(item_dict['K'])),
pose=QuaternionList.from_list(item_dict['pose']),
image_id=item_dict['image_id'],
category_id=item_dict['category_id'],
id=item_dict['id'])
def src_coco_img(self, src_coco_img: COCO_Image):
self.update_src_coco_img(src_coco_img=src_coco_img)
self.update_src_img(src_img=cv2.imread(self.src_coco_img.coco_url))
self.update_center(center=Point2D(x=int(src_coco_img.width / 2),
y=int(src_coco_img.height / 2)))
self.adjust_center_to_src_img()
def center_to_cener_of(self, img: np.ndarray):
img_h, img_w = img.shape[:2]
self.update_center(Point2D(x=int(img_w / 2), y=int(img_h / 2)))
def from_dict(cls, item_dict: dict) -> COCO_Zoom:
return COCO_Zoom(magnitude=item_dict['magnitude'],
center=Point2D.from_list(item_dict['center'])
if 'center' in item_dict else None)
)
cropped_mark_dataset.images.append(cropped_coco_image)
mark_ann_found = False
mark_ann_list = cast(List[COCO_Annotation], [])
for i in list(range(len(mark_anns)))[::-1]:
if roi_ann.bbox.contains(mark_anns[i].bbox):
mark_ann_found = True
# mark_ann = mark_anns[i].copy()
mark_ann_list.append( mark_anns[i].copy())
del mark_anns[i]
if not mark_ann_found:
raise Exception
for mark_ann in mark_ann_list:
mark_ann.segmentation = mark_ann.segmentation - Point2D(x=roi_ann.bbox.xmin, y=roi_ann.bbox.ymin)
mark_ann.bbox = mark_ann.segmentation.to_bbox()
mark_ann.keypoints = mark_ann.keypoints - Point2D(x=roi_ann.bbox.xmin, y=roi_ann.bbox.ymin)
cropped_coco_ann = COCO_Annotation(
id=len(cropped_mark_dataset.annotations),
category_id=mark_ann.category_id,
image_id=cropped_coco_image.id,
segmentation=mark_ann.segmentation,
bbox=mark_ann.bbox,
area=mark_ann.bbox.area(),
keypoints=mark_ann.keypoints,
num_keypoints=len(mark_ann.keypoints),
keypoints_3d=mark_ann.keypoints_3d
)
cropped_mark_dataset.annotations.append(cropped_coco_ann)
crop_pbar.update()
def _process_organized_part(self, organized_part: dict,
frame_img: np.ndarray,
whole_number_coco_image: COCO_Image,
measure_ann: COCO_Annotation,
whole_number_count: int, digit_dir: str):
whole_number_cat = self.whole_number_dataset.categories.get_unique_category_from_name(
'whole_number')
whole_number_seg = None
whole_number_abs_bbox = None
seg_len_list = [
len(part_ann.segmentation) for part_ann in organized_part['anns']
]
if all([seg_len > 0 for seg_len in seg_len_list]):
target = 'seg'
elif all([seg_len == 0 for seg_len in seg_len_list]):
target = 'bbox'
else:
logger.warning(
f"There are empty segmentations among organized_part['anns'].")
logger.warning(
f"Cannot determine whether to merge segmentations or bounding boxes."
)
logger.warning(
f"[len(part_ann.segmentation) for part_ann in organized_part['anns']]: {[len(part_ann.segmentation) for part_ann in organized_part['anns']]}"
)
logger.warning(
f"organized_part['whole_names']: {organized_part['whole_names']}"
)
logger.warning(
f"organized_part['part_names']: {organized_part['part_names']}"
)
logger.warning(f"Assuming target = 'bbox'")
target = 'bbox'
if target == 'seg':
for part_ann in organized_part['anns']:
whole_number_seg = whole_number_seg + part_ann.segmentation if whole_number_seg is not None else part_ann.segmentation
whole_number_abs_bbox = whole_number_seg.to_bbox().to_int()
elif target == 'bbox':
whole_number_seg = Segmentation()
for part_ann in organized_part['anns']:
whole_number_abs_bbox = whole_number_abs_bbox + part_ann.bbox if whole_number_abs_bbox is not None else part_ann.bbox
whole_number_abs_bbox = whole_number_abs_bbox.to_int()
else:
raise Exception
whole_number_bbox_region = frame_img[
whole_number_abs_bbox.ymin:whole_number_abs_bbox.ymax,
whole_number_abs_bbox.xmin:whole_number_abs_bbox.xmax, :]
whole_number_img_save_path = f'{digit_dir}/{get_rootname_from_filename(whole_number_coco_image.file_name)}_{whole_number_count}.{get_extension_from_filename(whole_number_coco_image.file_name)}'
self._save_image(img=whole_number_bbox_region,
save_path=whole_number_img_save_path)
digit_coco_image = COCO_Image.from_img_path(
img_path=whole_number_img_save_path,
license_id=0,
image_id=len(self.digit_dataset.images))
self.digit_dataset.images.append(digit_coco_image)
measure_bbox_ref_point = Point2D(x=measure_ann.bbox.xmin,
y=measure_ann.bbox.ymin)
if target == 'seg':
whole_number_seg = whole_number_seg - measure_bbox_ref_point # relative to measure bbox
whole_number_bbox = whole_number_seg.to_bbox(
) # relative to measure bbox
elif target == 'bbox':
whole_number_seg = Segmentation() # Empty
whole_number_bbox = whole_number_abs_bbox - measure_bbox_ref_point # relative to measure bbox
else:
raise Exception
whole_number_coco_ann = COCO_Annotation(
id=len(self.whole_number_dataset.annotations),
category_id=whole_number_cat.id,
image_id=whole_number_coco_image.id,
segmentation=whole_number_seg,
bbox=whole_number_bbox,
area=whole_number_bbox.area())
self.whole_number_dataset.annotations.append(whole_number_coco_ann)
for part_ann in organized_part['anns']:
part_ann = COCO_Annotation.buffer(part_ann)
coco_cat = self.categories.get_obj_from_id(part_ann.category_id)
digit_cat = self.digit_dataset.categories.get_unique_category_from_name(
coco_cat.name.split('part')[-1])
whole_number_bbox_ref_point = measure_bbox_ref_point + Point2D(
x=whole_number_bbox.xmin, y=whole_number_bbox.ymin)
if target == 'seg':
digit_seg = part_ann.segmentation - whole_number_bbox_ref_point
digit_bbox = digit_seg.to_bbox()
elif target == 'bbox':
digit_seg = Segmentation()
digit_bbox = part_ann.bbox - whole_number_bbox_ref_point
else:
raise Exception
digit_coco_ann = COCO_Annotation(id=len(
self.digit_dataset.annotations),
category_id=digit_cat.id,
image_id=digit_coco_image.id,
segmentation=digit_seg,
bbox=digit_bbox,
area=digit_bbox.area())
self.digit_dataset.annotations.append(digit_coco_ann)
def _process_single_digit_ann(self, frame_img: np.ndarray,
whole_number_coco_image: COCO_Image,
whole_ann: COCO_Annotation,
measure_ann: COCO_Annotation,
whole_number_count: int, digit_dir: str):
coco_cat = self.categories.get_obj_from_id(whole_ann.category_id)
whole_number_cat = self.whole_number_dataset.categories.get_unique_category_from_name(
'whole_number')
# Update Digit Image Handler
orig_bbox = whole_ann.bbox.to_rounded_int(special=True)
if orig_bbox.area() == 0:
logger.error(f'Encountered orig_bbox.area() == 0')
logger.error(f'orig_bbox: {orig_bbox}')
logger.error(f'whole_ann.bbox: {whole_ann.bbox}')
raise Exception
whole_number_bbox_region = frame_img[orig_bbox.ymin:orig_bbox.ymax,
orig_bbox.xmin:orig_bbox.xmax, :]
whole_number_img_save_path = f'{digit_dir}/{get_rootname_from_filename(whole_number_coco_image.file_name)}_{whole_number_count}.{get_extension_from_filename(whole_number_coco_image.file_name)}'
self._save_image(img=whole_number_bbox_region,
save_path=whole_number_img_save_path)
digit_coco_image = COCO_Image.from_img_path(
img_path=whole_number_img_save_path,
license_id=0,
image_id=len(self.digit_dataset.images))
self.digit_dataset.images.append(digit_coco_image)
# Update Whole Number Annotation Handler
measure_bbox = measure_ann.bbox
measure_bbox_ref_point = Point2D(x=measure_bbox.xmin,
y=measure_bbox.ymin)
if len(whole_ann.segmentation) != 0: # Segmentation Based
whole_number_seg = whole_ann.segmentation - measure_bbox_ref_point
whole_number_bbox = whole_number_seg.to_bbox()
else: # BBox Based (No Segmentation)
whole_number_seg = whole_ann.segmentation.copy()
whole_number_bbox = whole_ann.bbox - measure_bbox_ref_point
whole_number_coco_ann = COCO_Annotation(
id=len(self.whole_number_dataset.annotations),
category_id=whole_number_cat.id,
image_id=whole_number_coco_image.id,
segmentation=whole_number_seg,
bbox=whole_number_bbox,
area=whole_number_bbox.area())
self.whole_number_dataset.annotations.append(whole_number_coco_ann)
# Update Digit Annotation Handler
digit_cat = self.digit_dataset.categories.get_unique_category_from_name(
coco_cat.name)
whole_number_bbox_ref_point = measure_bbox_ref_point + Point2D(
x=whole_number_bbox.xmin, y=whole_number_bbox.ymin)
if len(whole_ann.segmentation) != 0: # Segmentation Based
digit_seg = whole_ann.segmentation - whole_number_bbox_ref_point
digit_bbox = digit_seg.to_bbox()
else: # BBox Based (No Segmentation)
digit_seg = whole_ann.segmentation.copy()
digit_bbox = whole_ann.bbox - whole_number_bbox_ref_point
digit_coco_ann = COCO_Annotation(id=len(
self.digit_dataset.annotations),
category_id=digit_cat.id,
image_id=digit_coco_image.id,
segmentation=digit_seg,
bbox=digit_bbox,
area=digit_bbox.area())
self.digit_dataset.annotations.append(digit_coco_ann)
from logger import logger
from common_utils.common_types.point import Point2D, Point3D, Point2D_List, Point3D_List
from common_utils.common_types.keypoint import Keypoint2D, Keypoint3D, Keypoint2D_List, Keypoint3D_List
from common_utils.common_types.bbox import BBox
from common_utils.common_types.segmentation import Polygon, Segmentation
const_int = 10
const_float = 20.0
pt2d_0 = Point2D(x=1.0, y=2.0)
pt2d_1 = Point2D(x=3.0, y=4.0)
assert pt2d_0 + pt2d_1 == Point2D(x=pt2d_0.x + pt2d_1.x, y=pt2d_0.y + pt2d_1.y)
assert pt2d_0 + const_int == Point2D(x=pt2d_0.x + const_int,
y=pt2d_0.y + const_int)
assert pt2d_0 + const_float == Point2D(x=pt2d_0.x + const_float,
y=pt2d_0.y + const_float)
assert pt2d_0 - pt2d_1 == Point2D(x=pt2d_0.x - pt2d_1.x, y=pt2d_0.y - pt2d_1.y)
assert pt2d_0 - const_int == Point2D(x=pt2d_0.x - const_int,
y=pt2d_0.y - const_int)
assert pt2d_0 - const_float == Point2D(x=pt2d_0.x - const_float,
y=pt2d_0.y - const_float)
assert pt2d_0 * const_int == Point2D(x=pt2d_0.x * const_int,
y=pt2d_0.y * const_int)
assert pt2d_0 * const_float == Point2D(x=pt2d_0.x * const_float,
y=pt2d_0.y * const_float)
assert pt2d_0 / const_int == Point2D(x=pt2d_0.x / const_int,
y=pt2d_0.y / const_int)
assert pt2d_0 / const_float == Point2D(x=pt2d_0.x / const_float,